Fault Diagnosis of Power Network based on Radial basis Function Neural Network
Yilin Wang · IOP Conference Series Materials Science and Engineering · 2018
This paper proposed a fault diagnosis method based on radial basis function neural network (RBF), and this mthod could effectively solve the diagnosis problem of connection lines between interconnected regions. This method divides the large power grid into several regions through the network overlapping partition, and after the fault occurs, the RBF neural network diagnosis module corresponding to the area corresponding to the alarm information is selectively triggered according to the alarm information. The simulation results show that this method can not only diagnose faults occurring in different regions, but also can effectively diagnose faults occurring in Interregional tie lines. This method is simple and effective, can make up for the shortcomings of the fault diagnosis method of the existing grid partition fault in the connection line fault diagnosis and handle all kinds of complex fault conditions, and has good fault tolerance ability.